

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase			:				Tuple   =     logging.get_logger(__name__)

lowercase			:				int   =     {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Dict								=	'''swin'''

							UpperCAmelCase_						:		Any								=	{
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(					self    ,						__UpperCAmelCase=2_24    ,						__UpperCAmelCase=4    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=96    ,						__UpperCAmelCase=[2, 2, 6, 2]    ,						__UpperCAmelCase=[3, 6, 12, 24]    ,						__UpperCAmelCase=7    ,						__UpperCAmelCase=4.0    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=0.0    ,						__UpperCAmelCase=0.0    ,						__UpperCAmelCase=0.1    ,						__UpperCAmelCase="gelu"    ,						__UpperCAmelCase=False    ,						__UpperCAmelCase=0.0_2    ,						__UpperCAmelCase=1E-5    ,						__UpperCAmelCase=32    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase    ,						)		->       Optional[Any]:
														super().__init__(**__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					image_size
														A      :					List[str]      		=					patch_size
														A      :					Dict      		=					num_channels
														A      :					Tuple      		=					embed_dim
														A      :					Dict      		=					depths
														A      :					Tuple      		=					len(__UpperCAmelCase						)
														A      :					List[str]      		=					num_heads
														A      :					Union[str, Any]      		=					window_size
														A      :					Tuple      		=					mlp_ratio
														A      :					Optional[int]      		=					qkv_bias
														A      :					List[Any]      		=					hidden_dropout_prob
														A      :					Union[str, Any]      		=					attention_probs_dropout_prob
														A      :					Optional[Any]      		=					drop_path_rate
														A      :					List[Any]      		=					hidden_act
														A      :					Any      		=					use_absolute_embeddings
														A      :					List[str]      		=					layer_norm_eps
														A      :					int      		=					initializer_range
														A      :					Dict      		=					encoder_stride
														# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														A      :					str      		=					int(embed_dim * 2 ** (len(__UpperCAmelCase						) - 1)						)
														A      :					Dict      		=					['''stem'''] + [f'stage{idx}' for idx in range(1    ,						len(__UpperCAmelCase						) + 1						)]
														A		,		A      :					List[Any]      		=					get_aligned_output_features_output_indices(
														    out_features=__UpperCAmelCase    ,						out_indices=__UpperCAmelCase    ,						stage_names=self.stage_names						)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Dict								=	version.parse('''1.11'''				)




							@property
							def       snake_case			(					self						)		->       Mapping[str, Mapping[int, str]]:
														return OrderedDict(
														    [
														        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
														    ]						)






							@property
							def       snake_case			(					self						)		->       float:
														return 1E-4









import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														super().__init__()
														self.register_modules(vqvae=__UpperCAmelCase    ,						unet=__UpperCAmelCase    ,						scheduler=__UpperCAmelCase						)






							@torch.no_grad()
							def __call__(					self    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 0.0    ,						__UpperCAmelCase = 50    ,						__UpperCAmelCase = "pil"    ,						__UpperCAmelCase = True    ,						**__UpperCAmelCase    ,						)		->       Union[Tuple, ImagePipelineOutput]:
														A      :					List[Any]      		=					randn_tensor(
														    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,						generator=__UpperCAmelCase    ,						)
														A      :					List[Any]      		=					latents.to(self.device						)

														# scale the initial noise by the standard deviation required by the scheduler
														A      :					Optional[int]      		=					latents * self.scheduler.init_noise_sigma

														self.scheduler.set_timesteps(__UpperCAmelCase						)

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														A      :					str      		=					'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

														A      :					List[str]      		=					{}
														if accepts_eta:
																					A      :					Optional[Any]      		=					eta

														for t in self.progress_bar(self.scheduler.timesteps						):
																					A      :					Any      		=					self.scheduler.scale_model_input(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					# predict the noise residual
																					A      :					Dict      		=					self.unet(__UpperCAmelCase    ,						__UpperCAmelCase						).sample
																					# compute the previous noisy sample x_t -> x_t-1
																					A      :					int      		=					self.scheduler.step(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						).prev_sample

														# decode the image latents with the VAE
														A      :					str      		=					self.vqvae.decode(__UpperCAmelCase						).sample

														A      :					Tuple      		=					(image / 2 + 0.5).clamp(0    ,						1						)
														A      :					int      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						).numpy()
														if output_type == "pil":
																					A      :					Union[str, Any]      		=					self.numpy_to_pil(__UpperCAmelCase						)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__UpperCAmelCase						)






